Deep learning-based video watermarking algorithms perform well in terms of robustness and perceptual quality. However, their resistance to HEVC compression remains a major limitation, especially under high compression ratios, where watermark extraction accuracy significantly degrades. To address this issue, this paper proposes a Spatio-temporally Enhanced Video Watermarking (SEVMark) based on invertible neural networks (INNs). SEVMark introduces a channel attention mechanism in the temporal domain to adaptively focus on keyframes, and employs spatial pyramid pooling module in the spatial domain to capture multi-scale features. These two modules work in tandem to enhance the spatio-temporal feature representation, achieving high robustness and imperceptibility. Furthermore, based on the HEVC encoding process, a HEVC video compression simulator (DiffH265) is designed and incorporated as a key component of the noise layer, guiding the encoder-decoder network to maintain high extraction accuracy under HEVC compression. Experimental results demonstrate that SEVMark outperforms state-of-the-art methods in both quantitative and qualitative evaluations, particularly demonstrating excellent robustness against HEVC compression attacks under high compression ratios.

Video steganography in the intra prediction mode (IPM) domain embeds secret messages by modifying IPM values. However, such modifications are highly susceptible to detection by video steganalysis techniques, particularly those leveraging recompression-based calibration features. In this paper, the signal restoration phenomenon that occurs during video recompression is first modeled, which reveals the underlying reason for the effectiveness of recompression calibration-based detection features. Based on this insight, a IPM priority-preserving strategy is proposed. This strategy integrates the steganographic modification state with the optimal IPM selection mechanism during recompression, employing dynamic cost revision and joint cost decomposition to guide steganographic modifications toward optimal selection. By aligning modifications with recompression tendency, the proposed method mitigates signal restoration effects, reduces distribution discrepancies in calibration-based detection features, and enhances overall steganographic security. Extensive experimental evaluations demonstrate that the proposed scheme significantly improves resistance against both intra-frame and inter-frame steganalysis features while maintaining superior visual quality and bitrate control.

Video steganography based on prediction unit (PU) embeds secret messages by modifying PU partition modes, where such modifications typically relying on additive embed ding distortion. However, additive distortion cannot accurately reflect sample-level distortion variations caused by inter-frame distortion propagation, leading to distortion drift across frames and making the embedded traces more susceptible to detection by existing video steganalysis. To address this issue, we analyze the mechanism of inter-frame distortion propagation and propose a steganographic modification strategy based on the Synchronizing Mode Directions (SMDs), which effectively mitigates the accumulation and spread of inter-frame distortion. Furthermore, to enhance the security of stego videos, a video frame-level directional distortion propagation model is constructed based on Distortion Propagation Chains (DPC). By constructing the DPC model, the propagation of inter-frame distortion resulting from steganography can be traced directionally, enabling dynamic correction of the cover cost. Experimental results demonstrate that the proposed method significantly enhances resistance to both intra-frame and inter-frame steganalysis, while maintaining high visual quality and effective bitrate control.

With the wide spread of video, video watermarking has become increasingly crucial for copyright protection and content authentication. However, video watermarking still faces numerous challenges. For example, existing methods typically have shortcomings in terms of watermarking capacity and robustness, and there is a lack of specialized noise layer for High Efficiency Video Coding(HEVC) compression. To address these issues, this paper introduces a Deep Invertible Network for Video watermarking (DINVMark) and designs a noise layer to simulate HEVC compression. This approach not only increases watermarking capacity but also enhances robustness. DINVMark employs an Invertible Neural Network (INN), where the encoder and decoder share the same network structure for both watermark embedding and extraction. This shared architecture ensures close coupling between the encoder and decoder, thereby improving the accuracy of the watermark extraction process. Experimental results demonstrate that the proposed scheme significantly enhances watermark robustness, preserves video quality, and substantially increases watermark embedding capacity.

To deal with the development of the distributed server, this paper proposes a new method for reversible data hiding in encrypted images based on the Chinese Remainder Theorem (CRT), encrypting and sharing one image to multiple data hiders through$(k,n)$-threshold secret sharing. First, an original image is divided into the most significant bit (MSB) compression area and the least significant bit (LSB) area by utilizing the spatial correlation. The$l$-MSB layers are predicted to obtain prediction errors, and these prediction errors are compressed by Huffman coding. Then according to the value of$k$, CRT and secret sharing scheme are performed on the$(8-l)$-LSB layers to generate the shared bitstream. Finally,$n$encrypted images for sharing consist of MSB compression bitstreams and shared bitstreams, whose size is adjusted based on$k$value. Each data hider can independently embed secret data after having one of the encrypted images, while the receiver can recover the original image only after receiving$k$or more encrypted images. Experimental results show that the proposed algorithm not only provides a large embedding space for secret data, but is also able to complete the inverse operation of data hiding and realize the lossless recovery of the original image with$(k,n)$-threshold secret sharing.

Pixel value ordering (PVO) is an efficient method for implementing reversible data hiding, which can achieve embedding based on overlapping pixel blocks when combined with the flexible patch moving (FPM) mode, especially the two-dimensional (2D) FPM mode. However, the existing 2D FPM mode, whose pairing way of prediction error is not conducive to generating more pixel blocks available for embedding, and whose movement rules are too inefficient to fully exploit the potential of the PVO, results in wasting many available blocks. Therefore, in this paper, a two-stage embedding mechanism is proposed for the 2D FPM mode, in which the combination of prediction errors is adjusted to improve the possibility of generating available blocks and the two-stage embedding doubles the number of pixel blocks available for embedding. Furthermore, an FPM mode selection is proposed, where four novel 2D FPM modes are designed to efficiently exploit the potential of the PVO according to the different directional gradients. Lastly, a set of efficient 2D mappings is well-designed for multiple histograms to achieve lower embedding distortion. The extensive experimental results show that the proposed method outperforms other state-of-the-art methods in terms of embedding capacity and image fidelity. The average peak signal-to-noise ratio for the Kodak image dataset is as high as 63.62 dB after embedding 10,000 bits.

With high embedding capacity and security, transform coefficient-based video steganography has become an important branch of video steganography. However, existing steganalysis methods against transform coefficient-based steganography provide insufficient consideration to the prediction process of HEVC compression, which results in steganalysis that is not straightforward and fail to effectively detect adaptive steganography methods in low embedding rate scenarios. In this paper, an HEVC video steganalysis method based on centralized error and attention mechanism against transform coefficient-based steganography is proposed. Firstly, the centralized error phenomenon brought by distortion compensation-based steganography is analyzed, and prediction error maps is constructed for steganalysis to achieve higher SNR(signal-to-noise ratio). Secondly, a video steganalysis network called CESNet (Centralized Error Steganalysis Network) is proposed. The network takes the prediction error maps as input and four types of convolutional modules are designed to adapt to different stages of feature extraction. To address the intra-frame sparsity of adaptive steganography, CEA (Centralized Error Attention) modules based on spatial and channel attention mechanisms are proposed to adaptively enhance the steganographic region. Finally, after extracting the feature vectors of each frame, the detection of steganographic video is completed using the self-attention mechanism. Experimental results show that compared with the existing transform coefficient-based video steganalysis methods, the proposed method can effectively detect multiple transform coefficient-based steganography algorithms and achieve higher detection performance in low payload scenarios.

HEVC (High Efficiency Video Coding) provides abundant embedding carriers for video steganography, leading to rapid development in the field of video steganography while increasing the urgent demand for video steganalysis. However, existing steganalysis methods against PU (prediction unit) based steganography primarily use the extraction of video statistical features, which ignore the potential information of each frame and fail to effectively detect different PU-based steganography methods. In this paper, a video steganalysis method based on PU maps and multi-scale convolutional residual network is proposed. Firstly, the effects of PU-based steganography on the spatial domain and the compressed domain are analyzed. It is observed that steganography has less impact on the spatial domain, whereas it significantly disrupts the connection between PU blocks in the compressed domain, leaving distinct steganographic traces. Consequently, the PU partition modes containing local connections are introduced to generate PU maps for steganalysis. Secondly, a video steganalysis network called PUSN (Prediction Unit Steganalysis Network) is constructed. The network takes PU maps as input and consists of three parts: feature extraction, feature representation, and binary classification. Additionally, a multi-scale module is proposed to enhance the detection performance. Finally, the detection result of the steganographic video is obtained by the voting mechanism. The experimental results show that compared with the existing steganalysis methods, the proposed method could effectively detect multiple PU-based steganography methods and achieve higher detection accuracy across various embedding rates.

With the increasing popularity of digital video, video steganography has become a hot research topic in the field of covert communication and privacy protection. The existing prediction unit (PU) based video steganography often tends to result in large bit rate increase, which is also easily noticeable to the steganography analyst. To solve this problem, an adaptive steganography for HEVC video based on attention-net and PU partition modes is proposed. First, the distortion of modified PUs is analyzed from the perspective of rate distortion optimization at the group of pictures (GOP) level, and we find that modifying PU will lead to distortion accumulation and abnormal bitrate increase. Therefore, an adaptive distortion function based on the improved rate distortion cost is designed, and the embedding distortion is minimized by using Syndrome-Trellis Code (STC) steganography coding. Meanwhile, a super-resolution convolutional neural network with non-local sparse attention-net filter is proposed to replace the in-loop filter in HEVC to reconstruct the reference frame, thereby reducing the bitrate cost and improving the visual quality of stego-video. Experimental results show that the proposed algorithm can achieve superior perceptual quality and bitrate performance comparing with the sate-of-the-art works.

Distortion compensation method is a common way to cope with the distortion drift problem in coefficient domain HEVC steganography. However, it will leave obvious steganographic traces called centralized error (CER). The current coefficient domain HEVC steganography is fragile to CER-based steganalysis. In this article, a novel adaptive HEVC steganography that can resist CER-based steganalysis is proposed. First, the difference of CER between H.264/AVC and HEVC is introduced, and the CER feature in HEVC is re-modeled. Then, from two aspects of overall average distribution and single-frame distribution, we conclude that there is a strong correlation among four components of the CER feature. Last, an adaptive cost function is proposed by maintaining one component distribution to resist steganalysis. Experimental results show that the proposed cost function can effectively improve the security compared with other coefficient-based HEVC steganography. In addition, the proposed steganography outperforms other HEVC steganography in visual quality and bit rate increase.

Modification of the optimal recursive block encoding process is commonly adopted in HEVC steganography based on block partitioning structure to embed secret messages, which inevitably disrupts the optimal rate distortion optimization process, resulting in a degradation of visual quality and an increase in bit rate. In this paper, we analyze the intra frame recursive block encoding process, categorizing modifications based on block partitioning structures into skip-level and non-skip-level modifications. Then, the rate distortion difference between these two types is compared. Additionally, the Maintenance Principle of Quad-tree Structure is introduced, which aims to preserve the stego quad-tree structure as closely as possible to the original one. Furthermore, a new cover mapping method is designed to expand the embedding capacity, and a quad-tree structure-preserving adaptive steganography is proposed. Extensive experimental results demonstrate that the proposed scheme can embed messages with fewer disruptions to the optimal rate distortion optimization process, ultimately improving the visual quality and reducing the bit rate growth.

Abstract Reversible data hiding in encrypted images (RDH‐EI) can be used as an efficient technique to directly embed additional data in the encrypted domain without violating privacy. In this study, a multi‐MSB (most significant bit) prediction‐based RDH‐EI scheme is proposed, which is completely reversible and has high embedding payload. An improved gradient adjustment predictor (GAP) is used for pixel prediction. A binary location map is employed to store the location of the prediction errors. Subsequently, pre‐processing is performed on accurately predicted pixels in the original image to store the prediction difference. The pre‐processed image is encrypted using a stream cipher, and then the data hider can substitute multi‐MSB values of each available pixel in encrypted image with secret message bits to obtain the marked encrypted image. On the recipient side, the secret message can be extracted without any error and the original image can be reconstructed losslessly. Experimental results show that not only the embedding payload is improved but also the hidden data can be extracted accurately, and the cover image can be recovered perfectly.

With the advent of sophisticated and low‐cost video editing software, digital videos are highly vulnerable to be tampered. The authenticity and integrity identification of digital videos is an urgent issue. In this study, an effective method to detect double High Efficiency Video Coding (HEVC) video compression with different quantisation parameter (QP) is proposed, which often occurs in the video tampering process. First, the effects of QP on the distributions of Discrete Cosine Transform (DCT) coefficients and Transform Unit (TU) size are analysed. Then a feature set including 17 features is derived from quantised DCT coefficients and TU size. It can characterize the statistical differences between single and double compressed videos. Finally, the Library for Support Vector Machine classifier is exploited to identify whether a given HEVC video has been double compressed or not. Experimental results demonstrate that the authors’ detection method has a good comprehensive performance.

In this paper, an efficient reversible data hiding method for encrypted image based on neighborhood prediction is proposed, which includes image encryption, reversible data hiding in encrypted domain, and hidden data extraction. The cover image is first partitioned into non-overlapping blocks, and then the pixel value in each block is encrypted by modulo operation. Therefore, the linear prediction difference in the block that satisfies the specific condition is consistent before and after encryption, ensuring that data extraction is completely separable from image decryption. In addition, by using the linear weighting of three adjacent pixels in the block to predict the current pixel, the prediction accuracy can be improved. The data-hider, who does not know the original image content, may embed additional data based on prediction difference histogram modification. Data extraction and image recovery are free of any error. Experimental results demonstrate the feasibility and efficiency of the proposed scheme.

An efficient method of completely separable reversible data hiding in encrypted images is proposed. The cover image is first partitioned into nonoverlapping blocks and specific encryption is applied to obtain the encrypted image. Then, image difference in the encrypted domain can be calculated based on the homomorphic property of the cryptosystem. The data hider, who does not know the original image content, may reversibly embed secret data into image difference based on two-dimensional difference histogram modification. Data extraction is completely separable from image decryption; that is, data extraction can be done either in the encrypted domain or in the decrypted domain, so that it can be applied to different application scenarios. In addition, data extraction and image recovery are free of any error. Experimental results demonstrate the feasibility and efficiency of the proposed scheme.

Digital video sometimes needs to be stored and processed in an encrypted format to maintain security and privacy. For the purpose of content notation and/or tampering detection, it is necessary to perform data hiding in these encrypted videos. In this way, data hiding in encrypted domain without decryption preserves the confidentiality of the content. In addition, it is more efficient without decryption followed by data hiding and re-encryption. In this paper, a novel scheme of data hiding directly in the encrypted version of H.264/AVC video stream is proposed, which includes the following three parts, i.e., H.264/AVC video encryption, data embedding, and data extraction. By analyzing the property of H.264/AVC codec, the codewords of intraprediction modes, the codewords of motion vector differences, and the codewords of residual coefficients are encrypted with stream ciphers. Then, a data hider may embed additional data in the encrypted domain by using codeword substitution technique, without knowing the original video content. In order to adapt to different application scenarios, data extraction can be done either in the encrypted domain or in the decrypted domain. Furthermore, video file size is strictly preserved even after encryption and data embedding. Experimental results have demonstrated the feasibility and efficiency of the proposed scheme.

An audio watermarking scheme based on wavelet packet and psychoacoustic model is presented. Wavelet packet is a very good tool to analysis the audio signal which is non-stationary. The algorithm has better imperceptibility by using masking effect in human auditory system. The masking threshold can be computed in wavelet domain. Thus the computational complexity is reduced greatly, because it doesn’t like MPEG algorithm which should perform FFT. The watermark can be blind extracted by using linear predictive coding. Experimental results show that the watermark is imperceptible and the algorithm is robust to many attacks, such as mp3 compression, noise addition, requantization, low pass filtering, D/A -A/D and so on.